# library(devtools)
# install_github('MacoskoLab/liger')
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(liger)
library(Rtsne)
library(reshape2)

Attaching package: ‘reshape2’

The following object is masked from ‘package:tidyr’:

    smiths
library(ggrepel)

Testing LIGER for integration of scATAC and scRNA data. Following vignette from (gitHub)[https://macoskolab.github.io/liger/walkthrough_rna_atac.html].

1. Download and load datasets:

ATAC data is preprocessed to gene-level features (no. of reads that overlap gene body and promoter for each gene). Data is downloaded from (here)[https://umich.app.box.com/s/5hkhpou4inrulo570yhc38ay8g3ont5b]

rna_clusts = readRDS("~/10X_data/rna_cluster_assignments.RDS")
atac_clusts = readRDS("~/10X_data/atac_cluster_assignments.RDS")
pbmc.atac <- readRDS('~/10X_data/pbmc.atac.expression.mat.RDS')
pbmc.rna <- readRDS('~/10X_data/pbmc.rna.expression.mat.RDS')

2. Create LIGER object

List of count matrices as input. createLiger converts to sparseMatrices.

pbmc.data = list(atac=pbmc.atac[,names(atac_clusts)], rna=pbmc.rna[,names(rna_clusts)])
int.pbmc <- createLiger(pbmc.data)
[1] "Removing 97 genes not expressing in atac."
[1] "Removing 11048 genes not expressing in rna."

3. Data preprocessing

## Normalize to column total (adj. for sequencing depth)
int.pbmc <- normalize(int.pbmc)
## Select highly variable genes ONLY IN THE RNA DATASET
int.pbmc <- selectGenes(int.pbmc, datasets.use = 2)
## Scale to the SD
int.pbmc <- scaleNotCenter(int.pbmc)

4. Run NMF

int.pbmc <- optimizeALS(int.pbmc, k=20)

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Converged in 3.941199 mins, 16 iterations.
Best results with seed 1.
smp <- sample(1:nrow(int.pbmc@H$atac), size = 1000)
smp_genes <- sample(1:length(int.pbmc@var.genes), size = 100)
int.pbmc@W[,smp_genes] %>% heatmap(Rowv = NA)
Error in int.pbmc@W[, smp_genes] %>% heatmap(Rowv = NA) : 
  could not find function "%>%"

Align factor

Dodgy quantile normalization

int.pbmc <- readRDS(file = "~/10X_data/pbmc.trainedLIGER.RDS")
cannot open compressed file '/home/jovyan/10X_data/pbmc.trainedLIGER.RDS', probable reason 'No such file or directory'Error in gzfile(file, "rb") : cannot open the connection

int.pbmc@scale.data$atac[,
                         smp_genes] %>%
  melt(varnames=c("cell", "gene")) %>%
  ggplot(aes(value, color=gene)) + geom_histogram() +
  facet_wrap(gene~.)
int.pbmc@scale.data$rna[,
                         smp_genes] %>%
  melt(varnames=c("cell", "gene")) %>%
  ggplot(aes(value, color=gene)) + geom_histogram() +
  facet_wrap(gene~.)

  

Visualize dataset-specific VS common latent factors

Even in dataset specific component there is separation of cell types (Meaningful information that is ATAC specific?)

atac.comp <- int.pbmc@H$atac[smp,] %*% int.pbmc@V$atac
Error: object 'smp' not found
rna.comp <- int.pbmc@H$rna[smp,] %*% int.pbmc@V$rna
rna.common.comp <- int.pbmc@H$rna[smp,] %*% int.pbmc@W
tsne.rna.comp <- Rtsne(rna.comp, pca=F, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.rna.comp$Y) <- rownames(rna.comp)
tsne.rna.common.comp <- Rtsne(rna.common.comp, pca=F, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.rna.common.comp$Y) <- rownames(rna.common.comp)
tsne.rna.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=rna_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

tsne.rna.common.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=rna_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

Compare weights of dataset specific and common factors

Are HVGs in RNA low coverage in ATAC?

Evaluate feature selection for ATAC-seq data

---
title: "Testing LIGER"
output: html_notebook
---

```{r}
# library(devtools)
# install_github('MacoskoLab/liger')
library(tidyverse)
library(liger)
library(Rtsne)
library(reshape2)
library(ggrepel)
```

Testing LIGER for integration of scATAC and scRNA data. Following vignette from (gitHub)[https://macoskolab.github.io/liger/walkthrough_rna_atac.html].

### 1. Download and load datasets: 
ATAC data is preprocessed to gene-level features (no. of reads that overlap gene body and promoter for each gene). Data is downloaded from (here)[https://umich.app.box.com/s/5hkhpou4inrulo570yhc38ay8g3ont5b]
```{r}
rna_clusts = readRDS("~/10X_data/rna_cluster_assignments.RDS")
atac_clusts = readRDS("~/10X_data/atac_cluster_assignments.RDS")
pbmc.atac <- readRDS('~/10X_data/pbmc.atac.expression.mat.RDS')
pbmc.rna <- readRDS('~/10X_data/pbmc.rna.expression.mat.RDS')
```

### 2. Create LIGER object
List of count matrices as input. `createLiger` converts to sparseMatrices. 
```{r}
pbmc.data = list(atac=pbmc.atac[,names(atac_clusts)], rna=pbmc.rna[,names(rna_clusts)])
int.pbmc <- createLiger(pbmc.data)

```
### 3. Data preprocessing
```{r}
## Normalize to column total (adj. for sequencing depth)
int.pbmc <- normalize(int.pbmc)
## Select highly variable genes ONLY IN THE RNA DATASET
int.pbmc <- selectGenes(int.pbmc, datasets.use = 2)
## Scale to the SD
int.pbmc <- scaleNotCenter(int.pbmc)
```

### 4. Run NMF 

```{r}
int.pbmc <- optimizeALS(int.pbmc, k=20)
```
```{r}
smp <- sample(1:nrow(int.pbmc@H$atac), size = 1000)
smp_genes <- sample(1:length(int.pbmc@var.genes), size = 100)

int.pbmc@W[,smp_genes] %>% heatmap(Rowv = NA)
int.pbmc@V$atac[,smp_genes] %>% heatmap(Rowv = NA)
```
```{r}

```

### Align factor
Dodgy quantile normalization
```{r}
int.pbmc <- quantileAlignSNF(int.pbmc)
# saveRDS(int.pbmc, file = "~/10X_data/pbmc.trainedLIGER.RDS")
int.pbmc <- readRDS(file = "~/my_data/10X_data/pbmc.trainedLIGER.RDS")
```

<!-- ### Visualize  -->
<!-- ```{r} -->
<!-- runMyTSNE <- function(object, use.raw = F, dims.use = 1:ncol(object@H.norm), use.pca = F, -->
<!--                     perplexity = 30, theta = 0.5, method = 'Rtsne', fitsne.path = NULL, -->
<!--                     rand.seed = 42) { -->
<!--   data.use <- do.call(rbind, object@H)   -->
<!--   set.seed(rand.seed) -->
<!--   object@tsne.coords <- Rtsne(object@H.norm[, dims.use], pca = use.pca, check_duplicates = F, -->
<!--                                 theta = theta, perplexity = perplexity)$Y -->
<!--   rownames(object@tsne.coords) <- rownames(data.use) -->
<!--   return(object) -->
<!-- } -->

```{r}
smp_genes <- sample(1:2000,10)
int.pbmc@scale.data$atac[,
                         smp_genes] %>%
  melt(varnames=c("cell", "gene")) %>%
  ggplot(aes(value, color=gene)) + geom_histogram() +
  facet_wrap(gene~.)

int.pbmc@scale.data$rna[,
                         smp_genes] %>%
  melt(varnames=c("cell", "gene")) %>%
  ggplot(aes(value, color=gene)) + geom_histogram() +
  facet_wrap(gene~.)
  
```


<!-- ``` -->

### Visualize dataset-specific VS common latent factors

Even in dataset specific component there is separation of cell types (Meaningful information that is ATAC specific?)

```{r}

atac.comp <- int.pbmc@H$atac[smp,] %*% int.pbmc@V$atac
atac.common.comp <- int.pbmc@H$atac[smp,] %*% int.pbmc@W

tsne.atac.comp <- Rtsne(atac.comp, pca=T, check_duplicates = F, theta=0.5, perplexity=10)
rownames(tsne.atac.comp$Y) <- rownames(atac.comp)
tsne.atac.common.comp <- Rtsne(atac.common.comp, pca=T, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.atac.common.comp$Y) <- rownames(atac.common.comp)


tsne.atac.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=atac_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

tsne.atac.common.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=atac_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

```

```{r}

rna.comp <- int.pbmc@H$rna[smp,] %*% int.pbmc@V$rna
rna.common.comp <- int.pbmc@H$rna[smp,] %*% int.pbmc@W

tsne.rna.comp <- Rtsne(rna.comp, pca=F, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.rna.comp$Y) <- rownames(rna.comp)
tsne.rna.common.comp <- Rtsne(rna.common.comp, pca=F, check_duplicates = F, theta=0.5, perplexity=30)
rownames(tsne.rna.common.comp$Y) <- rownames(rna.common.comp)


tsne.rna.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=rna_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

tsne.rna.common.comp$Y %>%
  as.tibble(rownames="cell") %>%
  mutate(clust=rna_clusts[cell]) %>%
  ggplot(aes(V1, V2, color=clust)) + 
  geom_point()

```

### Compare weights of dataset specific and common factors
```{r}
int.pbmc@W %>% 
  melt(varnames = c("factor", "gene")) %>%
  filter(factor==1) %>%
  mutate(rank=rank(value)) %>%
  ggplot(aes(rank, value)) + 
  geom_point() +
  geom_text_repel(data=. %>% filter(rank > 2300), aes(label=gene))
```
```{r}
V_atac <- int.pbmc@V$atac 
colnames(V_atac) <- colnames(int.pbmc@W)

V_rna <- int.pbmc@V$rna
colnames(V_rna) <- colnames(int.pbmc@W)

V_atac %>% 
  melt(varnames = c("factor", "gene")) %>%
  filter(factor==1) %>%
  mutate(rank=rank(value)) %>%
  ggplot(aes(rank, value)) + 
  geom_point() +
  geom_text_repel(data=. %>% filter(rank > 2300), aes(label=gene)) +
  ggtitle('top V ATAC')

V_rna %>% 
  melt(varnames = c("factor", "gene")) %>%
  group_by(factor) %>%
  mutate(rank=rank(value)) %>%
  ungroup() %>%
  ggplot(aes(rank, value)) + 
  geom_point() +
  # geom_text_repel(data=. %>% filter(rank > 2310), aes(label=gene)) +
  facet_wrap(factor~.) +
  ggtitle('top V ATAC')

```
```{r}
genes.bc <- read.table(file = "../my_data/cellranger-atac110_count_30439_WSSS8038360_GRCh38-1_1_0.genes_bc.bed", sep = "\t", as.is = c(1), header = FALSE)

genes.bc
```

### Are HVGs in RNA low coverage in ATAC?
```{r}

long_atac_raw <- 
  int.pbmc@raw.data$atac %>%
  as.matrix() %>%
  melt(varnames=c("gene", "cell")) %>%
  mutate(var_gene=ifelse(gene %in% int.pbmc@var.genes, T, F)) 

cell_smp <- sample(unique(long_atac_raw$cell), 500)
gene_smp <- sample(unique(long_atac_raw$gene), 1000)

long_atac_raw %>%
  filter(gene %in% gene_smp & cell %in% cell_smp) %>%
  ggplot(aes(value, fill=var_gene)) + 
  geom_histogram(bins=100) +
  facet_wrap(var_gene~., scales = "free_y") +
  coord_cartesian(xlim=c(0,30))


```

```{r}
long_atac_raw %>%
  filter(gene %in% gene_smp & cell %in% cell_smp) %>%
  mutate(zeroes=ifelse(value==0, T,F)) %>%
  ggplot(aes(var_gene, fill=zeroes)) + 
  geom_bar(position="fill")
```


### Evaluate feature selection for ATAC-seq data
```{r}
int.pbmc_hvg_rna <- selectGenes(int.pbmc, num.genes = 2000, datasets.use = 2)
int.pbmc_hvg_atac <- selectGenes(int.pbmc, num.genes = 2000, datasets.use = 1)

plot_meanAcc_hvgs <- function(int.pbmc){
  gene_means <- 
    int.pbmc@raw.data$atac %>%
    rowMeans()
  data.frame(meanAcc=gene_means, gene=names(gene_means)) %>%
     mutate(var_gene=ifelse(gene %in% int.pbmc@var.genes, "HVG", "other")) %>%
    ggplot(aes(fill=var_gene, meanAcc)) +
    geom_histogram(bins=50) +
    facet_wrap(var_gene~., scales="free_y", nrow=2, ncol=1)
  }

ggarrange(
  plot_meanAcc_hvgs(int.pbmc_hvg_rna) + ggtitle("HVG RNA"),
  plot_meanAcc_hvgs(int.pbmc_hvg_atac) + ggtitle("HVG ATAC"), common.legend = TRUE
  )
```








